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<h1 class="title toc-ignore">More on data frames</h1>
<h4 class="author"><em>Data Carpentry contributors</em></h4>
</div>
<hr />
<blockquote>
<h2>Learning Objectives</h2>
<ul>
<li>Extract values from a vector in R.</li>
<li>Perform operations on columns in a data frame.</li>
<li>Subset data in a data frame.</li>
</ul>
</blockquote>
<hr />
<!--
# What are data frames?
`data.frame` is the _de facto_ data structure for most tabular data and what we
use for statistics and plotting.
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feel=c("furry", "furry", "squishy", "spiny"),
weight=c(45, 8, 1.1, 0.8))
str(example_data)
```
```
## 'data.frame': 4 obs. of 3 variables:
## $ animal: Factor w/ 4 levels "cat","dog","sea cucumber",..: 2 1 3 4
## $ feel : Factor w/ 3 levels "furry","spiny",..: 1 1 3 2
## $ weight: num 45 8 1.1 0.8
```
```r
example_data <- data.frame(animal=c("dog", "cat", "sea cucumber", "sea urchin"),
feel=c("furry", "furry", "squishy", "spiny"),
weight=c(45, 8, 1.1, 0.8), stringsAsFactors=FALSE)
str(example_data)
```
```
## 'data.frame': 4 obs. of 3 variables:
## $ animal: chr "dog" "cat" "sea cucumber" "sea urchin"
## $ feel : chr "furry" "furry" "squishy" "spiny"
## $ weight: num 45 8 1.1 0.8
```
-->
<div id="indexing-and-sequences-within-a-vector" class="section level1">
<h1>Indexing and sequences (within a vector)</h1>
<p>If we want to extract one or several values from a vector, we must provide one or several indices in square brackets, just as we do in math.</p>
<!--
For instance:
```r
expression[2] # what level of expression is in the second element of the vector?
expression[c(3, 2)]
expression[2:4]
expression[c(3,2, 2:4)] # combining both what do you get?
```
-->
<p>R indexes start at 1. Programming languages like Fortran, MATLAB, and R start counting at 1, because that’s what human beings typically do. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that’s simpler for computers to do.</p>
<!--
`:` is a special function that creates numeric vectors of integer in increasing
or decreasing order, test `1:10` and `10:1` for instance. The function `seq()`
(for __seq__uence) can be used to create more complex patterns:
```r
seq(1, 10, by=2)
seq(5, 10, length.out=3) # equal breaks of sequence into vector length = length.out
seq(50, by=5, length.out=10) # sequence 50 by 5 until you hit vector length = length.out
seq(1, 8, by=3) # sequence by 3 until you hit 8
```
-->
<p>Our metadata data frame has rows and columns (it has 2 dimensions), if we want to extract some specific data from it, we need to specify the “coordinates” we want from it. Row numbers come first, followed by column numbers (i.e. [row, column]).</p>
<pre class="sourceCode r"><code class="sourceCode r">metadata[<span class="dv">1</span>, <span class="dv">2</span>] <span class="co"># first element in the 2nd column of the data frame</span>
metadata[<span class="dv">1</span>, <span class="dv">6</span>] <span class="co"># first element in the 6th column</span>
metadata[<span class="dv">1</span>:<span class="dv">3</span>, <span class="dv">7</span>] <span class="co"># first three elements in the 7th column</span>
metadata[<span class="dv">3</span>, ] <span class="co"># the 3rd element for all columns</span>
metadata[, <span class="dv">7</span>] <span class="co"># the entire 7th column</span>
head_meta <-<span class="st"> </span>metadata[<span class="dv">1</span>:<span class="dv">6</span>, ] <span class="co"># metadata[1:6, ] is equivalent to head(metadata)</span></code></pre>
<!--
### Challenge
1. The function `nrow()` on a `data.frame` returns the number of rows. Use it,
in conjuction with `seq()` to create a new `data.frame` called
`meta_by_2` that includes every other row of the survey data frame
starting at row 2 (2, 4, 6, ...)
```r
meta_by_2 <- metadata[seq(2, nrow(metadata), by=2), ]
```
--->
</div>
<div id="indexing-and-sequences-within-a-data.frame" class="section level1">
<h1>Indexing and sequences (within a <code>data.frame</code>)</h1>
<p>For larger datasets, it can be tricky to remember the column number that corresponds to a particular variable. (Are species names in column 5 or 7? oh, right… they are in column 6). In some cases, in which column the variable will be can change if the script you are using adds or removes columns. It’s therefore often better to use column names to refer to a particular variable, and it makes your code easier to read and your intentions clearer.</p>
<p>You can do operations on a particular column, by selecting it using the <code>$</code> sign. In this case, the entire column is a vector. You can use <code>names(metadata)</code> or <code>colnames(metadata)</code> to remind yourself of the column names. For instance, to extract all the strain information from our datasets:</p>
<pre class="sourceCode r"><code class="sourceCode r">metadata$strain</code></pre>
<p>In some cases, you may way to select more than one column. You can do this using the square brackets. Suppose we wanted strain and clade information:</p>
<pre class="sourceCode r"><code class="sourceCode r">metadata[, <span class="kw">c</span>(<span class="st">"strain"</span>, <span class="st">"clade"</span>)]</code></pre>
<p>You can even access columns by column name <em>and</em> select specific rows of interest. For example, if we wanted the strain and clade of just rows 4 through 7, we could do:</p>
<pre class="sourceCode r"><code class="sourceCode r">metadata[<span class="dv">4</span>:<span class="dv">7</span>, <span class="kw">c</span>(<span class="st">"strain"</span>, <span class="st">"clade"</span>)]</code></pre>
</div>
<hr/>
<p><a href="http://datacarpentry.org/">Data Carpentry</a>,
2017. <a href="LICENSE.html">License</a>. <a href="CONTRIBUTING.html">Contributing</a>. <br/>
Questions? Feedback?
Please <a href="https://github.com/datacarpentry/R-ecology-lesson/issues/new">file
an issue on GitHub</a>. <br/> On
Twitter: <a href="https://twitter.com/datacarpentry">@datacarpentry</a></p>
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